Cognitive Reactivity to Sad Mood Provocation and the Prediction of Depressive Relapse
Bibliographic record
Abstract
CONTEXT: Episode remission in unipolar major depression, while distinguished by minimal symptom burden, can also be a period of marked sensitivity to emotional stress as well as an increased risk of relapse. OBJECTIVE: To examine whether mood-linked changes in dysfunctional thinking predict relapse in recovered patients who were depressed. DESIGN: In phase 1 of this study, patients with major depressive disorder were randomly assigned to receive either antidepressant medication or cognitive behavior therapy. In phase 2, patients who achieved clinical remission underwent sad mood provocation and were then observed with regular clinical assessments for 18 months. SETTING: Outpatient psychiatric clinics at the Centre for Addiction and Mental Health, Toronto, Ontario. PARTICIPANTS: A total of 301 outpatients with major depressive disorder, aged 18 to 65 years, participated in phase 1 of this study and 99 outpatients with major depressive disorder in remission, aged 18 to 65 years, participated in phase 2. MAIN OUTCOME MEASURE: Occurrence of a relapse meeting DSM-IV criteria for a major depressive episode as assessed by the longitudinal interval follow-up evaluation and a Hamilton Depression Rating Scale score of 16 or greater. RESULTS: Patients who recovered through antidepressant medication showed greater cognitive reactivity following the mood provocation than those who received cognitive behavior therapy. Regardless of type of prior treatment, the magnitude of mood-linked cognitive reactivity was a significant predictor of relapse over the subsequent 18 months. Patients whose mood-linked endorsement of dysfunctional attitudes increased by a minimum of 8 points had a significantly shorter time to relapse than those whose scores were not as elevated. CONCLUSIONS: The vulnerability of remitted depressed patients for illness relapse may be related to the (re)activation of depressive thinking styles triggered by temporary dysphoric states. This is the first study to link such differences to prognosis following successful treatment for depression. Further understanding of factors predisposing to relapse/recurrence in recovered patients may help to shorten the potentially lifelong course of depression.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".